An Empirical Study on Perceptually Masking Privacy in Graph Visualizations

Jia Kai Chou, Chris Bryan, Jing Li, Kwan Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Researchers such as sociologists create visualizations of multivariate node-link diagrams to present findings about the relationships in communities. Unfortunately, such visualizations can inadvertently expose the ostensibly private identities of the persons that make up the dataset. By purposely violating graph readability metrics for a small region of the graph, we conjecture that local, exposed privacy leaks may be perceptually masked from easy recognition. In particular, we consider three commonly known metrics∗edge crossing, node clustering, and node-edge overlapping∗as a strategy to hide leaks. We evaluate the effectiveness of violating these metrics by conducting a user study that measures subject performance at visually searching for and identifying a privacy leak. Results show that when more masking operations are applied, participants needed more time to locate the privacy leak, though exhaustive, brute force search can eventually find it. We suggest future directions on how perceptual masking can be a viable strategy, primarily where modifying the underlying network structure is unfeasible.

Original languageEnglish (US)
Title of host publication2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018
EditorsStoney Trent, Jorn Kohlhammer, Graig Sauer, Robert Gove, Daniel Best, Celeste Lyn Paul, Nicolas Prigent, Diane Staheli
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538681947
DOIs
StatePublished - May 7 2019
Externally publishedYes
Event2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018 - Berlin, Germany
Duration: Oct 22 2018 → …

Publication series

Name2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018

Conference

Conference2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018
CountryGermany
CityBerlin
Period10/22/18 → …

Fingerprint

Visualization

Keywords

  • Human-centered computing
  • Visualization
  • Visualization design and evaluation methods

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Chou, J. K., Bryan, C., Li, J., & Ma, K. L. (2019). An Empirical Study on Perceptually Masking Privacy in Graph Visualizations. In S. Trent, J. Kohlhammer, G. Sauer, R. Gove, D. Best, C. L. Paul, N. Prigent, ... D. Staheli (Eds.), 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018 [8709181] (2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VIZSEC.2018.8709181

An Empirical Study on Perceptually Masking Privacy in Graph Visualizations. / Chou, Jia Kai; Bryan, Chris; Li, Jing; Ma, Kwan Liu.

2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018. ed. / Stoney Trent; Jorn Kohlhammer; Graig Sauer; Robert Gove; Daniel Best; Celeste Lyn Paul; Nicolas Prigent; Diane Staheli. Institute of Electrical and Electronics Engineers Inc., 2019. 8709181 (2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chou, JK, Bryan, C, Li, J & Ma, KL 2019, An Empirical Study on Perceptually Masking Privacy in Graph Visualizations. in S Trent, J Kohlhammer, G Sauer, R Gove, D Best, CL Paul, N Prigent & D Staheli (eds), 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018., 8709181, 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018, Berlin, Germany, 10/22/18. https://doi.org/10.1109/VIZSEC.2018.8709181
Chou JK, Bryan C, Li J, Ma KL. An Empirical Study on Perceptually Masking Privacy in Graph Visualizations. In Trent S, Kohlhammer J, Sauer G, Gove R, Best D, Paul CL, Prigent N, Staheli D, editors, 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8709181. (2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018). https://doi.org/10.1109/VIZSEC.2018.8709181
Chou, Jia Kai ; Bryan, Chris ; Li, Jing ; Ma, Kwan Liu. / An Empirical Study on Perceptually Masking Privacy in Graph Visualizations. 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018. editor / Stoney Trent ; Jorn Kohlhammer ; Graig Sauer ; Robert Gove ; Daniel Best ; Celeste Lyn Paul ; Nicolas Prigent ; Diane Staheli. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018).
@inproceedings{189390809c4b4bfb906f6e38aa841678,
title = "An Empirical Study on Perceptually Masking Privacy in Graph Visualizations",
abstract = "Researchers such as sociologists create visualizations of multivariate node-link diagrams to present findings about the relationships in communities. Unfortunately, such visualizations can inadvertently expose the ostensibly private identities of the persons that make up the dataset. By purposely violating graph readability metrics for a small region of the graph, we conjecture that local, exposed privacy leaks may be perceptually masked from easy recognition. In particular, we consider three commonly known metrics∗edge crossing, node clustering, and node-edge overlapping∗as a strategy to hide leaks. We evaluate the effectiveness of violating these metrics by conducting a user study that measures subject performance at visually searching for and identifying a privacy leak. Results show that when more masking operations are applied, participants needed more time to locate the privacy leak, though exhaustive, brute force search can eventually find it. We suggest future directions on how perceptual masking can be a viable strategy, primarily where modifying the underlying network structure is unfeasible.",
keywords = "Human-centered computing, Visualization, Visualization design and evaluation methods",
author = "Chou, {Jia Kai} and Chris Bryan and Jing Li and Ma, {Kwan Liu}",
year = "2019",
month = "5",
day = "7",
doi = "10.1109/VIZSEC.2018.8709181",
language = "English (US)",
series = "2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Stoney Trent and Jorn Kohlhammer and Graig Sauer and Robert Gove and Daniel Best and Paul, {Celeste Lyn} and Nicolas Prigent and Diane Staheli",
booktitle = "2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018",

}

TY - GEN

T1 - An Empirical Study on Perceptually Masking Privacy in Graph Visualizations

AU - Chou, Jia Kai

AU - Bryan, Chris

AU - Li, Jing

AU - Ma, Kwan Liu

PY - 2019/5/7

Y1 - 2019/5/7

N2 - Researchers such as sociologists create visualizations of multivariate node-link diagrams to present findings about the relationships in communities. Unfortunately, such visualizations can inadvertently expose the ostensibly private identities of the persons that make up the dataset. By purposely violating graph readability metrics for a small region of the graph, we conjecture that local, exposed privacy leaks may be perceptually masked from easy recognition. In particular, we consider three commonly known metrics∗edge crossing, node clustering, and node-edge overlapping∗as a strategy to hide leaks. We evaluate the effectiveness of violating these metrics by conducting a user study that measures subject performance at visually searching for and identifying a privacy leak. Results show that when more masking operations are applied, participants needed more time to locate the privacy leak, though exhaustive, brute force search can eventually find it. We suggest future directions on how perceptual masking can be a viable strategy, primarily where modifying the underlying network structure is unfeasible.

AB - Researchers such as sociologists create visualizations of multivariate node-link diagrams to present findings about the relationships in communities. Unfortunately, such visualizations can inadvertently expose the ostensibly private identities of the persons that make up the dataset. By purposely violating graph readability metrics for a small region of the graph, we conjecture that local, exposed privacy leaks may be perceptually masked from easy recognition. In particular, we consider three commonly known metrics∗edge crossing, node clustering, and node-edge overlapping∗as a strategy to hide leaks. We evaluate the effectiveness of violating these metrics by conducting a user study that measures subject performance at visually searching for and identifying a privacy leak. Results show that when more masking operations are applied, participants needed more time to locate the privacy leak, though exhaustive, brute force search can eventually find it. We suggest future directions on how perceptual masking can be a viable strategy, primarily where modifying the underlying network structure is unfeasible.

KW - Human-centered computing

KW - Visualization

KW - Visualization design and evaluation methods

UR - http://www.scopus.com/inward/record.url?scp=85066411763&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066411763&partnerID=8YFLogxK

U2 - 10.1109/VIZSEC.2018.8709181

DO - 10.1109/VIZSEC.2018.8709181

M3 - Conference contribution

AN - SCOPUS:85066411763

T3 - 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018

BT - 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018

A2 - Trent, Stoney

A2 - Kohlhammer, Jorn

A2 - Sauer, Graig

A2 - Gove, Robert

A2 - Best, Daniel

A2 - Paul, Celeste Lyn

A2 - Prigent, Nicolas

A2 - Staheli, Diane

PB - Institute of Electrical and Electronics Engineers Inc.

ER -